2013/03/26 by Jaewook Kang, Kang, Jaewook, Heung-No Lee +3
Computer Science · Engineering · #FOS: Computer and information sciences #Geophysical Methods and Applications #Image and Signal Denoising Methods #Information Theory (cs.IT) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1303.6388
openalex publication_date 2013/03/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper investigates the problem of sparse support detection (SSD) via a detection-oriented algorithm named Bayesian hypothesis test via belief propagation (BHT-BP). Our main focus is to compare BHT-BP to an estimation-based algorithm, called CS-BP, and show its superiority in the SSD problem. For this investigation, we perform a phase transition (PT) analysis over the plain of the noise level and signal magnitude on the signal support. This PT analysis sharply specifies the required signal magnitude for the detection under a certain noise level. In addition, we provide an experimental validation to assure the PT analysis. Our analytical and experimental results show the fact that BHT-BP detects the signal support against additive noise more robustly than CS-BP does.